Some practical Runge-Kutta formulas
Mathematics of Computation
Parallelization of the GESIMA mesoscale atmospheric model
Parallel Computing - Special issue on applications: parallel computing in regional weather modeling
Design of high performance financial modelling environment
Parallel Computing - Special issue on parallel computing in economics, finance and decision-making
PSBLAS: a library for parallel linear algebra computation on sparse matrices
ACM Transactions on Mathematical Software (TOMS)
Parallel processing for finite-difference modelling of ice sheets
Computers & Geosciences
Three parallel programming paradigms: comparisons on an archetypal PDE computation
Progress in computer research
3D finite difference computation on GPUs using CUDA
Proceedings of 2nd Workshop on General Purpose Processing on Graphics Processing Units
Exploiting graphical processing units for data-parallel scientific applications
Concurrency and Computation: Practice & Experience
Shallow water simulations on multiple GPUs
PARA'10 Proceedings of the 10th international conference on Applied Parallel and Scientific Computing - Volume 2
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Finite difference methods continue to provide an important and parallelisable approach to many numerical simulations problems. Iterative multigrid and multilevel algorithms can converge faster than ordinary finite difference methods but can be more difficult to parallelise. Data parallel paradigms tend to lend themselves particularly well to solving regular mesh PDEs whereby low latency communications and high compute to communications ratios can yield high levels of computational efficiency and raw performance. We report on some practical algorithmic and data layout approaches and on performance data on a range of Graphical Processing Units (GPUs) with CUDA. We focus on the use of multiple GPU devices with a single CPU host.